{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "img[alt=recurrent_unrolled] { width: 400px; }\n",
       "</style>\n",
       "<style>\n",
       "img[alt=sequence_vector] { width: 400px; }\n",
       "</style>\n",
       "<style>\n",
       "img[alt=gru-cell] { width: 400px; }\n",
       "</style>\n",
       "<style>\n",
       "img[alt=encoder-decoder] { width: 400px; }\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "img[alt=recurrent_unrolled] { width: 400px; }\n",
    "</style>\n",
    "<style>\n",
    "img[alt=sequence_vector] { width: 400px; }\n",
    "</style>\n",
    "<style>\n",
    "img[alt=gru-cell] { width: 400px; }\n",
    "</style>\n",
    "<style>\n",
    "img[alt=encoder-decoder] { width: 400px; }\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intro\n",
    "* Use case: arbitrary-length **sequence** data analysis - *anticipation* abilities\n",
    "* RNNs much like feed-forward NNs, but also with backward-facing connections\n",
    "* At time step *t* each node sees input *x(t)* plus its previous output *y(t-1)*.\n",
    "* Below: \"unrolling\" a net across a time axis.\n",
    "![recurrent_unrolled](pics/recurrent-neurons-unrolled.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Memory Cells\n",
    "* A network node that preserves state across time is called a **cell** (memory cell).\n",
    "* *h(t)* is a cell's \"hidden\" state at time=t.\n",
    "![recurrent-memcells](pics/recurrent-memcells.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input/Output Sequences\n",
    "* RNNs can be used to predict the results of time shifts (sequence-to-sequence), a sentiment score (sequence-to-vector), or image caption (vector-to-sequence).\n",
    "* sequence-to-vector nets = **encoders**; vector-to-sequence nets = **decoders**. One use case: language translation.\n",
    "* Below:\n",
    "    * Top Left: Sequence-to-sequence\n",
    "    * Top Right: Sequence-to-vector\n",
    "    * Bot Left:  Vector-to-sequence\n",
    "    * Bot Right: Delayed-sequence-to-sequence\n",
    "![sequence_vector](pics/sequence-vector.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic RNNs in TF\n",
    "* RNN design: layer of **5 recurrent cells** with tanh activation; runs over **2** time steps, and uses **vectors of size=3** at each step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "n_inputs = 3\n",
    "n_neurons = 5\n",
    "\n",
    "# two-layer net\n",
    "\n",
    "X0 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "X1 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "\n",
    "Wx = tf.Variable(tf.random_normal(shape=[n_inputs, n_neurons],dtype=tf.float32))\n",
    "Wy = tf.Variable(tf.random_normal(shape=[n_neurons,n_neurons],dtype=tf.float32))\n",
    "\n",
    "b = tf.Variable(tf.zeros([1, n_neurons], dtype=tf.float32))\n",
    "\n",
    "Y0 = tf.tanh(tf.matmul(X0, Wx) + b)\n",
    "Y1 = tf.tanh(tf.matmul(Y0, Wy) + tf.matmul(X1, Wx) + b)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to feed inputs at both time steps,\n",
    "\n",
    "import numpy as np\n",
    "# Mini-batch: instance 0,instance 1,instance 2,instance 3\n",
    "\n",
    "X0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]]) # t = 0\n",
    "X1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]]) # t = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output at t=0:\n",
      " [[-0.77183092 -0.99924457  0.23752896 -0.63130957 -0.83723265]\n",
      " [-0.92028087 -1.          0.99004787 -0.87230623 -0.99995315]\n",
      " [-0.97358704 -1.          0.999919   -0.95966864 -1.        ]\n",
      " [ 0.99999094 -0.99890459  0.9991411   0.99996841 -0.99999803]] \n",
      " output at t=1\n",
      " [[ 0.99512661 -1.          0.99997395 -0.99830353 -1.        ]\n",
      " [ 0.99977976  0.99013239 -0.96352106 -0.99476629  0.97579277]\n",
      " [ 0.99981618 -0.99989575  0.99114233 -0.99827981 -0.99984008]\n",
      " [ 0.54805535 -0.84061396 -0.99912792 -0.47432473 -0.99921536]]\n"
     ]
    }
   ],
   "source": [
    "# Y0, Y1 = network outputs at both time steps\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})\n",
    "    \n",
    "print(\"output at t=0:\\n\",Y0_val,\"\\n\",\"output at t=1\\n\",Y1_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unrolling through Time (Static) using static_rnn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "n_inputs = 3\n",
    "n_neurons = 5\n",
    "\n",
    "X0 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "X1 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "\n",
    "# BasicRNNCell() -- memcell \"factory\"\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(\n",
    "    num_units=n_neurons)\n",
    "\n",
    "# static_rnn() -- creates unrolled RNN net by chaining cells.\n",
    "# returns 1) python list of output tensors for each time step\n",
    "#         2) tensor of final network states\n",
    "\n",
    "output_seqs, states = tf.contrib.rnn.static_rnn(\n",
    "    basic_cell, \n",
    "    [X0, X1], \n",
    "    dtype=tf.float32)\n",
    "\n",
    "Y0, Y1 = output_seqs\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to feed inputs at both time steps,\n",
    "\n",
    "import numpy as np\n",
    "# Mini-batch: instance 0,instance 1,instance 2,instance 3\n",
    "\n",
    "X0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]]) # t = 0\n",
    "X1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]]) # t = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output at t=0:\n",
      " [[ 0.42442048  0.92431569 -0.2353479  -0.90074939 -0.94408685]\n",
      " [ 0.73783255  0.98977458 -0.72123086 -0.99919385 -0.99999249]\n",
      " [ 0.89336294  0.99865782 -0.9186905  -0.99999398 -1.        ]\n",
      " [-0.99143326 -0.99993676 -0.37607926  0.88796568 -0.99899191]] \n",
      " output at t=1\n",
      " [[ 0.81709599  0.48319042 -0.96708876 -0.9998284  -1.        ]\n",
      " [-0.18962485 -0.81231028 -0.21763545  0.88739753  0.57306314]\n",
      " [ 0.17130674 -0.6411857  -0.86380148 -0.95413983 -0.99999553]\n",
      " [-0.07749119 -0.86547101 -0.00461033 -0.91877526 -0.99582738]]\n"
     ]
    }
   ],
   "source": [
    "# Y0, Y1 = network outputs at both time steps\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})\n",
    "    \n",
    "print(\"output at t=0:\\n\",Y0_val,\"\\n\",\"output at t=1\\n\",Y1_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simplification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "n_steps = 2\n",
    "n_inputs = 3\n",
    "n_neurons = 5\n",
    "\n",
    "# this time, use placeholder with add'l dimension for #timesteps\n",
    "#X0 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "#X1 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "X =   tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "\n",
    "#print(X)\n",
    "\n",
    "# transpose - make time steps = 1st dimension\n",
    "# unstack - extract list of tensors\n",
    "\n",
    "X_seqs = tf.unstack(\n",
    "    tf.transpose(\n",
    "        X, perm=[1, 0, 2]))\n",
    "\n",
    "#print(X_seqs)\n",
    "\n",
    "# BasicRNNCell() -- memcell \"factory\"\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(\n",
    "    num_units=n_neurons)\n",
    "\n",
    "# static_rnn() -- creates unrolled RNN net by chaining cells.\n",
    "# returns 1) python list of output tensors for each time step\n",
    "#         2) tensor of final network states\n",
    "\n",
    "output_seqs, states = tf.contrib.rnn.static_rnn(\n",
    "    basic_cell, \n",
    "    X_seqs, \n",
    "    dtype=tf.float32)\n",
    "\n",
    "#Y0, Y1 = output_seqs\n",
    "\n",
    "# stack - merge output tensors\n",
    "# transpose - swap 1st two dimensions\n",
    "# returns tensor shape [none, #steps, #neurons]\n",
    "\n",
    "outputs = tf.transpose(\n",
    "    tf.stack(output_seqs), \n",
    "    perm=[1,0,2])\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.76157701  0.11581181  0.64773971 -0.79434019 -0.86054337]\n",
      "  [ 0.99998951 -0.66595364  0.99812627 -1.          0.84574401]]\n",
      "\n",
      " [[ 0.99683905  0.29572889  0.98365188 -0.99992883 -0.88169324]\n",
      "  [ 0.41841054 -0.92049074 -0.64612901 -0.73361856  0.29283327]]\n",
      "\n",
      " [[ 0.99996316  0.45685658  0.99936479 -1.         -0.89980829]\n",
      "  [ 0.99907684 -0.87088716  0.94328976 -0.9999997   0.87934762]]\n",
      "\n",
      " [[ 0.12318966  0.02264917  0.99982244 -0.99998975  0.99996465]\n",
      "  [ 0.9525854  -0.56515652  0.08665188 -0.99705428  0.87525886]]]\n"
     ]
    }
   ],
   "source": [
    "X_batch = np.array([\n",
    "        # t = 0      t = 1 \n",
    "        [[0, 1, 2], [9, 8, 7]], # instance 1\n",
    "        [[3, 4, 5], [0, 0, 0]], # instance 2\n",
    "        [[6, 7, 8], [6, 5, 4]], # instance 3\n",
    "        [[9, 0, 1], [3, 2, 1]], # instance 4\n",
    "    ])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val = outputs.eval(feed_dict={X: X_batch})\n",
    "    \n",
    "print(outputs_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Above code still not ideal - builds graph with one cell per time step. Ugly & can cause Out Of Memory errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unrolling through Time using dynamic_rnn()\n",
    "* uses while_loop() to iterate over the memcell\n",
    "* set swap_memory=True to move GPU memory to CPU during backprop if needed\n",
    "* accepts single tensor, outputs single tensor - no stack/unstack/transpose ops required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.01341763 -0.10483158 -0.94257653  0.83843452 -0.20272173]\n",
      "  [ 0.99978089 -0.63150525 -0.99999148  0.99999386 -0.87993085]]\n",
      "\n",
      " [[ 0.94205797 -0.13386673 -0.9997741   0.99812031 -0.64444101]\n",
      "  [-0.6134249  -0.55738503  0.39783546  0.89031053  0.04465704]]\n",
      "\n",
      " [[ 0.99817288 -0.16267382 -0.99999928  0.99997997 -0.86824256]\n",
      "  [ 0.99097538 -0.61533296 -0.99695957  0.99986053 -0.64558744]]\n",
      "\n",
      " [[ 0.9963541   0.23641461  0.75174934  0.98267573 -0.97034496]\n",
      "  [ 0.85169196 -0.07830215 -0.3604137   0.95550352  0.12307668]]]\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(\n",
    "    num_units=n_neurons)\n",
    "\n",
    "outputs, states = tf.nn.dynamic_rnn(\n",
    "    basic_cell, X, dtype=tf.float32)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val = outputs.eval(feed_dict={X: X_batch})\n",
    "    \n",
    "print(outputs_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable-Length Input Sequences\n",
    "* Most problems will have variable length inputs (like sentences).\n",
    "* This option uses **sequence_length** param (1D tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.28581977 -0.77421445 -0.34181327 -0.87767971 -0.91387445]\n",
      "  [ 0.99970448 -1.          0.79238343 -1.         -0.9997654 ]]\n",
      "\n",
      " [[ 0.96786171 -0.99937457 -0.03243476 -0.99988878 -0.99875116]\n",
      "  [ 0.          0.          0.          0.          0.        ]]\n",
      "\n",
      " [[ 0.99903995 -0.99999839  0.28328663 -0.99999982 -0.99998271]\n",
      "  [ 0.96896154 -0.99999189  0.43341497 -0.99996883 -0.98279852]]\n",
      "\n",
      " [[ 0.9976812  -0.99999118  0.99979782 -0.99983948  0.84931362]\n",
      "  [ 0.57188803 -0.99268627 -0.30526906 -0.99518502  0.109933  ]]]\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "\n",
    "seq_length = tf.placeholder(tf.int32, [None])\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(\n",
    "    num_units=n_neurons)\n",
    "\n",
    "outputs, states = tf.nn.dynamic_rnn(\n",
    "    basic_cell, X, dtype=tf.float32,\n",
    "    #\n",
    "    #\n",
    "    sequence_length=seq_length)\n",
    "    #\n",
    "    #\n",
    "X_batch = np.array([\n",
    "        [[0, 1, 2], [9, 8, 7]], # instance 1\n",
    "        [[3, 4, 5], [0, 0, 0]], # instance 2 -- zero padded\n",
    "        [[6, 7, 8], [6, 5, 4]], # instance 3\n",
    "        [[9, 0, 1], [3, 2, 1]], # instance 4\n",
    "    ])\n",
    "\n",
    "seq_length_batch = np.array([2,1,2,2])\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val, states_val = sess.run(\n",
    "        [outputs, states], \n",
    "        feed_dict={X: X_batch, seq_length: seq_length_batch})\n",
    "\n",
    "# RNN should output zero vectors for any time step \n",
    "# beyond input sequence length\n",
    "print(outputs_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.99970448 -1.          0.79238343 -1.         -0.9997654 ]\n",
      " [ 0.96786171 -0.99937457 -0.03243476 -0.99988878 -0.99875116]\n",
      " [ 0.96896154 -0.99999189  0.43341497 -0.99996883 -0.98279852]\n",
      " [ 0.57188803 -0.99268627 -0.30526906 -0.99518502  0.109933  ]]\n"
     ]
    }
   ],
   "source": [
    "# states tensor contains final state of each cell\n",
    "print(states_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable-Length Output Sequences\n",
    "* Typical output sequence lengths not equal to input lengths\n",
    "* Most common solution: use *end-of-sequence (EOS) token*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RNN Training\n",
    "* Unroll through time (as shown above) then use backprop through time (*BPTT*).\n",
    "![rnn_training](pics/rnn-training.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RNN Training: Classifier\n",
    "* Example: use MNIST (CNN would be better, but lets keep it simple)\n",
    "* Treat images as 28 rows of 28 pixels each\n",
    "* Use 150 rnn cells + fully-connected layer of 10 cells (1 per class)\n",
    "* Followed by softmax layer\n",
    "![sequence-classifier](pics/sequence-classifier.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# similar to MNIST classifier\n",
    "# unrolled RNN replaces hidden layers\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "from tensorflow.contrib.layers import fully_connected\n",
    "\n",
    "n_steps = 28\n",
    "n_inputs = 28\n",
    "n_neurons = 150\n",
    "n_outputs = 10\n",
    "learning_rate = 0.001\n",
    "\n",
    "# y = placeholder for target classes\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.int32, [None])\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(\n",
    "    num_units=n_neurons)\n",
    "\n",
    "outputs, states = tf.nn.dynamic_rnn(\n",
    "    basic_cell, X, dtype=tf.float32)\n",
    "\n",
    "logits = fully_connected(\n",
    "    states, n_outputs, activation_fn=None)\n",
    "\n",
    "xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "    labels=y, logits=logits)\n",
    "\n",
    "loss = tf.reduce_mean(\n",
    "    xentropy)\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(\n",
    "    learning_rate=learning_rate)\n",
    "\n",
    "training_op = optimizer.minimize(\n",
    "    loss)\n",
    "\n",
    "correct = tf.nn.in_top_k(\n",
    "    logits, y, 1)\n",
    "\n",
    "accuracy = tf.reduce_mean(\n",
    "    tf.cast(correct, tf.float32))\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# load MNIST data, reshape to [batch_size, n_steps, n_inputs]\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "mnist = input_data.read_data_sets(\"/tmp/data/\")\n",
    "\n",
    "X_test = mnist.test.images.reshape((-1, n_steps, n_inputs))\n",
    "y_test = mnist.test.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Train accuracy: 0.953333 Test accuracy: 0.8711\n",
      "1 Train accuracy: 0.953333 Test accuracy: 0.9417\n",
      "2 Train accuracy: 0.953333 Test accuracy: 0.9432\n",
      "3 Train accuracy: 0.946667 Test accuracy: 0.9595\n",
      "4 Train accuracy: 0.98 Test accuracy: 0.9627\n",
      "5 Train accuracy: 0.966667 Test accuracy: 0.9666\n",
      "6 Train accuracy: 0.96 Test accuracy: 0.961\n",
      "7 Train accuracy: 0.973333 Test accuracy: 0.9729\n",
      "8 Train accuracy: 0.986667 Test accuracy: 0.9702\n",
      "9 Train accuracy: 0.986667 Test accuracy: 0.9732\n"
     ]
    }
   ],
   "source": [
    "# ready to run. reshape each training batch before feeding to net.\n",
    "\n",
    "n_epochs = 10\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        for iteration in range(mnist.train.num_examples // batch_size):\n",
    "            \n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            X_batch = X_batch.reshape(\n",
    "                (-1, n_steps, n_inputs))\n",
    "\n",
    "            sess.run(\n",
    "                training_op, \n",
    "                feed_dict={X: X_batch, y: y_batch})\n",
    "            \n",
    "        acc_train = accuracy.eval(\n",
    "            feed_dict={X: X_batch, y: y_batch})\n",
    "        acc_test = accuracy.eval(\n",
    "            feed_dict={X: X_test, y: y_test})\n",
    "\n",
    "        print(epoch, \n",
    "              \"Train accuracy:\", acc_train, \n",
    "              \"Test accuracy:\",  acc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RNN Training: Predicting Time Series\n",
    "![rnn-timeseries](pics/rnn-timeseries.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t_min, t_max = 0, 30\n",
    "resolution = 0.1\n",
    "\n",
    "def time_series(t):\n",
    "    return t * np.sin(t) / 3 + 2 * np.sin(t*5)\n",
    "\n",
    "def next_batch(batch_size, n_steps):\n",
    "    t0 = np.random.rand(batch_size, 1) * (t_max - t_min - n_steps * resolution)\n",
    "    Ts = t0 + np.arange(0., n_steps + 1) * resolution\n",
    "    ys = time_series(Ts)\n",
    "    return ys[:, :-1].reshape(-1, n_steps, 1), ys[:, 1:].reshape(-1, n_steps, 1)\n",
    "\n",
    "t = np.linspace(t_min, t_max, (t_max - t_min) // resolution)\n",
    "\n",
    "n_steps = 20\n",
    "t_instance = np.linspace(\n",
    "    12.2, 12.2 + resolution * (n_steps + 1), n_steps + 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 20, 100)\n"
     ]
    }
   ],
   "source": [
    "# each training instance = 20 inputs long\n",
    "# targets = 20-input sequences\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "n_steps = 20\n",
    "n_inputs = 1\n",
    "n_neurons = 100\n",
    "n_outputs = 1\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\n",
    "\n",
    "cell = tf.contrib.rnn.BasicRNNCell(\n",
    "    num_units=n_neurons, \n",
    "    activation=tf.nn.relu)\n",
    "\n",
    "outputs, states = tf.nn.dynamic_rnn(\n",
    "    cell, X, dtype=tf.float32)\n",
    "\n",
    "print(outputs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# output at each time step now vector[100],\n",
    "# but we want single output value at each step.\n",
    "\n",
    "# use OutputProjectionWrapper()\n",
    "# -- adds FC layer to top of each output\n",
    "\n",
    "cell = tf.contrib.rnn.OutputProjectionWrapper(\n",
    "    tf.contrib.rnn.BasicRNNCell(\n",
    "        num_units=n_neurons, \n",
    "        activation=tf.nn.relu),\n",
    "    output_size=n_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define cost function using MSE\n",
    "# use Adam optimizer\n",
    "\n",
    "learning_rate = 0.001\n",
    "loss = tf.reduce_mean(\n",
    "    tf.square(outputs - y))\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(\n",
    "    learning_rate=learning_rate)\n",
    "\n",
    "training_op = optimizer.minimize(loss)\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tMSE: 15.3099\n",
      "100 \tMSE: 13.5276\n",
      "200 \tMSE: 11.0956\n",
      "300 \tMSE: 9.91156\n",
      "400 \tMSE: 14.0311\n",
      "500 \tMSE: 9.73811\n",
      "600 \tMSE: 9.23351\n",
      "700 \tMSE: 9.64445\n",
      "800 \tMSE: 8.98904\n",
      "900 \tMSE: 10.849\n",
      "[[[ 0.          0.          0.         ...,  0.          0.          0.        ]\n",
      "  [ 0.          0.04218276  0.         ...,  0.          0.          0.        ]\n",
      "  [ 0.          0.14342034  0.         ...,  0.          0.          0.        ]\n",
      "  ..., \n",
      "  [ 6.67315388  0.          6.39087296 ...,  6.9017005   6.30435514\n",
      "    6.23329258]\n",
      "  [ 6.61708975  0.          6.31429434 ...,  6.58116341  6.19745445\n",
      "    6.11896658]\n",
      "  [ 5.9406209   0.          5.73649979 ...,  5.63920403  5.5386672\n",
      "    5.47510672]]]\n"
     ]
    }
   ],
   "source": [
    "# initialize & run\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "n_iterations = 1000\n",
    "batch_size = 50\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        X_batch, y_batch = next_batch(batch_size, n_steps)\n",
    "        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        if iteration % 100 == 0:\n",
    "            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "            print(iteration, \"\\tMSE:\", mse)\n",
    "\n",
    "    \n",
    "    # use trained model to make some predictions\n",
    "    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
    "    y_pred = sess.run(outputs, feed_dict={X: X_new})\n",
    "    print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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d4ugjPJfadDfVSVwJhYWwaRNceGGYFxaG1jGqk8h86t1Rskckcsh9hLfug/6h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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1ff356908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.title(\"Testing the model\", fontsize=14)\n",
    "\n",
    "plt.plot(\n",
    "    t_instance[:-1], \n",
    "    time_series(t_instance[:-1]), \n",
    "    \"bo\", markersize=10, label=\"instance\")\n",
    "\n",
    "plt.plot(\n",
    "    t_instance[1:], \n",
    "    time_series(t_instance[1:]), \n",
    "    \"w*\", markersize=10, label=\"target\")\n",
    "\n",
    "plt.plot(\n",
    "    t_instance[1:], \n",
    "    y_pred[0,:,0], \n",
    "    \"r.\", markersize=10, label=\"prediction\")\n",
    "\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"Time\")\n",
    "#save_fig(\"time_series_pred_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **OutputProjectionWrapper()** = simplest solution for reducing output sequences to one value/timestep, but not most efficient.\n",
    "* More efficient solution shown below - **signficant speed boost**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Reshape:0\", shape=(?, 100), dtype=float32)\n",
      "Tensor(\"fully_connected/BiasAdd:0\", shape=(?, 1), dtype=float32)\n",
      "Tensor(\"Reshape_1:0\", shape=(?, 20, 1), dtype=float32)\n",
      "0 \tMSE: 22963.7\n",
      "100 \tMSE: 743.444\n",
      "200 \tMSE: 276.131\n",
      "300 \tMSE: 117.955\n",
      "400 \tMSE: 53.3529\n",
      "500 \tMSE: 63.4189\n",
      "600 \tMSE: 45.1415\n",
      "700 \tMSE: 41.5129\n",
      "800 \tMSE: 53.4219\n",
      "900 \tMSE: 43.2203\n",
      "[[[-3.46527553]\n",
      "  [-2.46867704]\n",
      "  [-1.10144436]\n",
      "  [ 0.69717044]\n",
      "  [ 2.08823276]\n",
      "  [ 3.13628578]\n",
      "  [ 3.55210543]\n",
      "  [ 3.4186697 ]\n",
      "  [ 2.85978389]\n",
      "  [ 2.15520501]\n",
      "  [ 1.67705297]\n",
      "  [ 1.6919663 ]\n",
      "  [ 1.93633199]\n",
      "  [ 2.70151305]\n",
      "  [ 3.87054777]\n",
      "  [ 5.11770582]\n",
      "  [ 6.15701818]\n",
      "  [ 6.71814394]\n",
      "  [ 6.69798708]\n",
      "  [ 6.08309698]]]\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "n_steps = 20\n",
    "n_inputs = 1\n",
    "n_neurons = 100\n",
    "n_outputs = 1\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\n",
    "\n",
    "cell = tf.contrib.rnn.BasicRNNCell(\n",
    "    num_units=n_neurons, \n",
    "    activation=tf.nn.relu)\n",
    "\n",
    "rnn_outputs, states = tf.nn.dynamic_rnn(\n",
    "    cell, X, dtype=tf.float32)\n",
    "\n",
    "# stack outputs using reshape\n",
    "stacked_rnn_outputs = tf.reshape(\n",
    "    rnn_outputs, [-1, n_neurons])\n",
    "\n",
    "print(stacked_rnn_outputs)\n",
    "\n",
    "# add FC layer -- just a projection, so no activation fn needed\n",
    "stacked_outputs = fully_connected(\n",
    "    stacked_rnn_outputs, \n",
    "    n_outputs,\n",
    "    activation_fn=None)\n",
    "\n",
    "print(stacked_outputs)\n",
    "\n",
    "# unstack outputs using reshape\n",
    "outputs = tf.reshape(\n",
    "    stacked_outputs, [-1, n_steps, n_outputs])\n",
    "\n",
    "print(outputs)\n",
    "\n",
    "loss = tf.reduce_sum(tf.square(outputs - y))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "#initialize & run\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "n_iterations = 1000\n",
    "batch_size = 50\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        X_batch, y_batch = next_batch(batch_size, n_steps)\n",
    "        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        if iteration % 100 == 0:\n",
    "            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "            print(iteration, \"\\tMSE:\", mse)\n",
    "\n",
    "    \n",
    "    # use trained model to make some predictions\n",
    "    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
    "    y_pred = sess.run(outputs, feed_dict={X: X_new})\n",
    "    print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QSBF/GVrCq6eVZmSfbqXele4ktVeMmQ0HJgLVHaxbZGY1Zlazd+/eZJ5WYpTC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\nRHKMAruISI5RYBcRyTH/H+iR+UZEn7pXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1d9bbdfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Testing the model\", fontsize=14)\n",
    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
    "plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creative RNNs\n",
    "* Use model to generate creative sequences\n",
    "* Provide seed sequence of length = n_steps, zero-filled\n",
    "* use model to append predicted new value to sequence\n",
    "* feed last n_steps values to model to predict next value, etc.\n",
    "* should get new sequence resembling original time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tMSE: 14607.1\n",
      "100 \tMSE: 505.605\n",
      "200 \tMSE: 167.29\n",
      "300 \tMSE: 83.1336\n",
      "400 \tMSE: 58.9695\n",
      "500 \tMSE: 61.0224\n",
      "600 \tMSE: 55.8671\n",
      "700 \tMSE: 43.7078\n",
      "800 \tMSE: 57.2013\n",
      "900 \tMSE: 55.3992\n",
      "1000 \tMSE: 54.082\n",
      "1100 \tMSE: 55.48\n",
      "1200 \tMSE: 39.4618\n",
      "1300 \tMSE: 40.7414\n",
      "1400 \tMSE: 47.8548\n",
      "1500 \tMSE: 43.9252\n",
      "1600 \tMSE: 47.892\n",
      "1700 \tMSE: 42.0762\n",
      "1800 \tMSE: 48.2429\n",
      "1900 \tMSE: 42.7509\n"
     ]
    },
    {
     "data": {
      "image/png": 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WFlCrG0q7drUg5h4znDlGFEiH0sxxpaoWVN3ndu9uVeHMcaXOKgbuSilgk51m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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1ff3853c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_iterations = 2000\n",
    "batch_size = 50\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        X_batch, y_batch = next_batch(batch_size, n_steps)\n",
    "        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        if iteration % 100 == 0:\n",
    "            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "            print(iteration, \"\\tMSE:\", mse)\n",
    "\n",
    "    sequence1 = [0. for i in range(n_steps)]\n",
    "    for iteration in range(len(t) - n_steps):\n",
    "        X_batch = np.array(sequence1[-n_steps:]).reshape(1, n_steps, 1)\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n",
    "        sequence1.append(y_pred[0, -1, 0])\n",
    "\n",
    "    sequence2 = [time_series(i * resolution + t_min + (t_max-t_min/3)) for i in range(n_steps)]\n",
    "    for iteration in range(len(t) - n_steps):\n",
    "        X_batch = np.array(sequence2[-n_steps:]).reshape(1, n_steps, 1)\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n",
    "        sequence2.append(y_pred[0, -1, 0])\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plt.plot(t, sequence1, \"b-\")\n",
    "plt.plot(t[:n_steps], sequence1[:n_steps], \"b-\", linewidth=3)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Value\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(t, sequence2, \"b-\")\n",
    "plt.plot(t[:n_steps], sequence2[:n_steps], \"b-\", linewidth=3)\n",
    "plt.xlabel(\"Time\")\n",
    "#save_fig(\"creative_sequence_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep RNNs\n",
    "![deep-rnn](pics/deep-rnn.png)\n",
    "* Built by stacking cells into a *MultiRNNCell()*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl.BasicRNNCell object at 0x7fd1ff3dbb00>\n",
      "<tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl.MultiRNNCell object at 0x7fd1d9b7c9e8>\n",
      "(2, 5, 100)\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "n_inputs = 2\n",
    "n_neurons = 100\n",
    "n_layers = 3\n",
    "n_steps = 5\n",
    "keep_prob = 0.5\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(\n",
    "    num_units=n_neurons)\n",
    "\n",
    "print(basic_cell)\n",
    "\n",
    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(\n",
    "    [basic_cell] * n_layers)\n",
    "\n",
    "print(multi_layer_cell)\n",
    "\n",
    "# states = tuple (one tensor/layer, = final state of layer's cell)\n",
    "\n",
    "outputs, states = tf.nn.dynamic_rnn(\n",
    "    multi_layer_cell, X, dtype=tf.float32)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "import numpy.random as rnd\n",
    "X_batch = rnd.rand(2, n_steps, n_inputs)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val, states_val = sess.run(\n",
    "        [outputs, states], \n",
    "        feed_dict={X: X_batch})\n",
    "    \n",
    "print(outputs_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DRNNs: Multiple GPUs\n",
    "* **TO DO**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropout\n",
    "* Very deep RNNs = danger of overfit. Use dropout to avoid problem.\n",
    "* Can apply before or after RNN\n",
    "* If applying dropout between RNN layers, need to use *DropoutWrapper*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# apply 50% dropout to inputs of RNN layers\n",
    "# can apply dropout to outputs via output_keep_prob\n",
    "\n",
    "tf.reset_default_graph()\n",
    "from tensorflow.contrib.layers import fully_connected\n",
    "\n",
    "n_inputs = 1\n",
    "n_neurons = 100\n",
    "n_layers = 3\n",
    "n_steps = 20\n",
    "n_outputs = 1\n",
    "\n",
    "keep_prob = 0.5\n",
    "learning_rate = 0.001\n",
    "\n",
    "def deep_rnn_with_dropout(X, y, is_training):\n",
    "\n",
    "    # TF implementation of DropoutWrapper doesn't differentiate\n",
    "    # between training & testing.\n",
    "    \n",
    "    cell = tf.contrib.rnn.BasicRNNCell(\n",
    "        num_units=n_neurons)\n",
    "    \n",
    "    if is_training:\n",
    "        cell = tf.contrib.rnn.DropoutWrapper(\n",
    "            cell, input_keep_prob=keep_prob)\n",
    "    \n",
    "    #\n",
    "    #\n",
    "    \n",
    "    multi_layer_cell = tf.contrib.rnn.MultiRNNCell(\n",
    "        [cell] * n_layers)\n",
    "    \n",
    "    rnn_outputs, states = tf.nn.dynamic_rnn(\n",
    "        multi_layer_cell, X, dtype=tf.float32)\n",
    "\n",
    "    stacked_rnn_outputs = tf.reshape(\n",
    "        rnn_outputs, [-1, n_neurons])\n",
    "    \n",
    "    stacked_outputs = fully_connected(\n",
    "        stacked_rnn_outputs, n_outputs, activation_fn=None)\n",
    "    \n",
    "    outputs = tf.reshape(\n",
    "        stacked_outputs, [-1, n_steps, n_outputs])\n",
    "\n",
    "    loss = tf.reduce_sum(\n",
    "        tf.square(outputs - y))\n",
    "    \n",
    "    optimizer = tf.train.AdamOptimizer(\n",
    "        learning_rate=learning_rate)\n",
    "    \n",
    "    training_op = optimizer.minimize(loss)\n",
    "\n",
    "    return outputs, loss, training_op\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\n",
    "outputs, loss, training_op = deep_rnn_with_dropout(X, y, is_training)\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Dropout, in this code, works during both training & testing (don't want).\n",
    "* *dropout_wrapper()* doesn't know how to handle this, so you need one graph for training, another for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tMSE: 10428.8\n",
      "100 \tMSE: 314.521\n",
      "200 \tMSE: 152.328\n",
      "300 \tMSE: 155.774\n",
      "400 \tMSE: 100.226\n",
      "500 \tMSE: 80.2064\n",
      "600 \tMSE: 92.3898\n",
      "700 \tMSE: 55.4301\n",
      "800 \tMSE: 50.8537\n",
      "900 \tMSE: 47.1413\n",
      "1000 \tMSE: 57.1007\n",
      "1100 \tMSE: 64.2314\n",
      "1200 \tMSE: 51.3272\n",
      "1300 \tMSE: 51.1612\n",
      "1400 \tMSE: 41.0518\n",
      "1500 \tMSE: 42.267\n",
      "1600 \tMSE: 29.6838\n",
      "1700 \tMSE: 48.4316\n",
      "1800 \tMSE: 46.5584\n",
      "1900 \tMSE: 40.6252\n"
     ]
    }
   ],
   "source": [
    "n_iterations = 2000\n",
    "batch_size = 50\n",
    "\n",
    "is_training = True\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    if is_training:\n",
    "        init.run()\n",
    "        for iteration in range(n_iterations):\n",
    "            X_batch, y_batch = next_batch(batch_size, n_steps)\n",
    "            sess.run(\n",
    "                training_op, \n",
    "                feed_dict={X: X_batch, y: y_batch})\n",
    "            \n",
    "            if iteration % 100 == 0:\n",
    "                mse = loss.eval(\n",
    "                    feed_dict={X: X_batch, y: y_batch})\n",
    "                \n",
    "                print(iteration, \"\\tMSE:\", mse)\n",
    "                \n",
    "        save_path = saver.save(sess, \"/tmp/my_model.ckpt\")\n",
    "        \n",
    "    else:\n",
    "        saver.restore(sess, \"/tmp/my_model.ckpt\")\n",
    "        \n",
    "        X_new = time_series(\n",
    "            np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
    "        y_pred = sess.run(\n",
    "            outputs, feed_dict={X: X_new})\n",
    "        \n",
    "        plt.title(\"Testing the model\", fontsize=14)\n",
    "        plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
    "        plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
    "        plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
    "        plt.legend(loc=\"upper left\")\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XLYObbw5e43TjFGIQ2N39feAdMzspsmgSkHJ92B5qYHd3Dhw4cMjHUWAXOXQtuwUYNQqe\neSboFmDBgqC6Ohkf7W8mOzu4UXDDDcFrnII6xK5VzPeApWa2GSgEfhyj/XabBQsWUFlZSWFhIXPn\nzmXSpEmMGDGCoUOHNj7xuX37dk466ST+5V/+hSFDhvDOO+/wwAMPcOKJJzJmzBguu+wyrrzySgCq\nqqo477zzGD16NKNHj+ZPf/oT27dv595772Xx4sUUFhayfv36RJ6ySEpJxUf7E6YzXUDGeupqt73x\n9NZbb/nJJ5/s7u51dXW+e/dud3evqqryAQMG+IEDB/ytt95yM/OXXnrJ3d137tzp/fr18127dvn+\n/ft93Lhx/t1I/50zZszw9evXu7v722+/7QMHDnR39xtvvNFvu+22bjuvRF9XEYkdOtltb8o9edod\n3J3rrruOP/7xj2RlZbFz587GLnb79evHKaecAsArr7zC6aefzpFHHgnA+eefz7Zt24CgK96moyp9\n8sknVMeiP2ERkQ4osLdi6dKlVFVV8eqrr5KTk0N+fj779u0DIK9lk6U2HDhwgJdffpmePXvGM6si\nIgdJqd4d46lp17u7d+/m85//PDk5OTz//PO8/fbbrW4zevRo1q1bx0cffUR9fT2/+c1vGtedeeaZ\n3H333Y3zDf2qt+ziV0Qk1hTYI4466ijGjh3LkCFD2LhxIxUVFQwdOpT//M//bLPr3S984Qtcd911\njBkzhrFjx5Kfn88RRxwBwF133UVFRQUFBQUMHjyYe++9F4Czzz6blStX6uapiMRNynXbm2yqq6sJ\nhULU19czbdo0Zs+ezbRp0xKdrUapel1F5GCd7bZXJfYo3XTTTRQWFjJkyBD69+/P1772tURnSUQy\nnG6eRqlhtCMRkWShEruISJpRYBeRbhXNuKXSOQrsItJtSkuhoAAevC/M3BNWM+21hfzrP6/mwfvC\nFBTEbdyJjKM6dhHpFmkxbmmKUIk9jkKRXv/fffddpk+f3m7aO+64g9ra2sb5s846i48/1kBUkj4a\nxi0NryrlK6FyeuwNholrHLd0VSk1NVBXl+icpr7UDezhMKxeDQsXBq/hcDcd9tCPc9xxx7FixYp2\n07QM7E899RR9+vQ55GOJJKuUH7c0haRmYG8Y8XvGDLjxxuB18uSog/v27dsZOHAgM2fOZNCgQUyf\nPp3a2lry8/O59tprGTFiBMuXL6eyspIpU6YwcuRIxo8fz9bI0OhvvfUWX/7ylxk6dCg33HBDs/0O\nGTIkkvUw8+fPZ8iQIRQUFHD33Xdz11138e677zJx4kQmTpwIQH5+Ph988AEAixYtYsiQIQwZMoQ7\n7rijcZ+DBg3isssu4+STT+bMM89k7969UZ2/SDyl/LilqaQzXUDGeoq6295Vq9xDIfdgvO9gCoWC\n5VF46623HPAXXnjB3d0vvvhiv+2227xfv35+6623NqY744wzfNu2be7u/vLLL/vEiRPd3f3ss8/2\nX/3qV+7uvmTJEs/Ly2vcb0OXwD//+c/9vPPO87q6Ond337Vrl7u79+vXz6uqqhqP0TBfUVHhQ4YM\n8erqat+zZ48PHjzY//KXv/hbb73l2dnZvmHDBnd3P//88/2RRx456JzUba8ki169gn/Vs8+q9/oJ\nk4L/WTP3UMjrJ0zys8+qd3Dv3TvROU1edLLb3tQsscdxxO/jjz+esWPHAjBr1ixeeOEFAC644AIg\n6ELgxRdf5Pzzz6ewsJArrriC9957D4A//elPzJgxA4AL2xh88ZlnnuGKK66gR4/gvnVDl79teeGF\nF5g2bRp5eXmEQiG+/vWvN/Yx079/fwojg+GOHDmS7du3R3HmIvGV8uOWppDUDOxxHPHbzFqdb+iu\n98CBA/Tp04eNGzc2Tlu2bGlz+3g6/PDDG99nZ2dTX1/fbccWOVQpP25pCknNwB7HEb//9re/8dJL\nLwHw6KOPMm7cuGbre/fuTf/+/Vm+fDkQVGVt2rQJgLFjx/LYY48BQZ/urfnqV7/KL37xi8Yg/OGH\nHwJtd+c7fvx4nnjiCWpra6mpqWHlypWMHz8+6vMU6W5pMW5pikjNwB7HEb9POukk7rnnHgYNGsRH\nH33Ed77znYPSLF26lAceeIBhw4Zx8sknN46Jeuedd3LPPfcwdOhQdu7c2er+L730Ur74xS9SUFDA\nsGHDePTRRwG4/PLLmTJlSuPN0wYjRozgoosuYsyYMRQVFXHppZcyfPjwqM9TJBE0bmn3ULe9TWzf\nvp2SkhJee+21hOYjlpLhuopIbKjbXhGRDKXA3kR+fn5aldZFJDMlVWBPRLVQOtP1FMlMSRPYe/bs\nya5duxSMYsTd2bVrFz179kx0VkSkmyVN7459+/Zlx44dVFVVJToraaNnz5707ds30dkQkW6WNIE9\nJyeH/v37JzobIiIpL2mqYkREJDYU2EVE0owCu4hImlFgFxFJMwrsIiJpJmaB3cyyzWyDma2O1T5F\nROTQxbLE/n1gS4epRCSlVVbCnDnQty+sXBm8zpkTLJfkEJPAbmZ9ganA/bHYn4gkp9JSKCiA+++H\niRNh2jSYMCGYLygI1kvixarEfgfwA+BAWwnM7HIzqzCzCj1dKpJ6Kith+nSorYVwXZhrBq2GhQu5\nZtBqwnVhamuD9Sq5J17UT56aWQnwf+7+qplNaCudu/8S+CUE/bFHe1wR6V7790eGGg6HYfJk/Mfl\nUFtDQW4e4UlFjYPdbN2a6JxKLErsY4FzzGw78Bhwhpn9Ogb7FZEkMn9+JLCXlkJ5OVZTDe7Ba3k5\nlJZSUxOMbSqJFXVgd/d/c/e+7p4PfBN4zt1nRZ0zEUkqpaVQUgL7yzdEInwTNTXsf2UjU6fCmjWJ\nyZ98Ru3YJfbCYVgd1L+yenUwLykvFIK1a+Ena4bjuXnN1nluHj8pLWTduiCdJFZMe3d097XA2lju\nU1JL5bYwdWdMpu+75eRRQw157DiuiJznyhhwYvSDjUvizJoVtH5584RiwnlFZFcEdezk5hEeVcSb\nxxWTswkuvDDRORWV2CVmSkvhBwWlfGFnOSGvxtwJeTVf2FnODwpK1RQuxc2bBzk5MPuybOzpMipv\nWcbD/W+m8pZl2NNlXHxpNjk5MHduonMqCuwSEw1N4QZ/uoFcmte/5lLDoE83dtgUTg++JLcBA2DF\nCqiuhmuvy+bEq0uY/b83cNK8EhZcn01NTbB+wIBE51QU2CUmGprCLVw1nOxQ8/rX7FAet6wqpKYG\n6upa314PvqSG4mIYPBj27YNevSArK6hT37s3WF5cnOgcCoAlYozRUaNGeUVFRbcfV+Jn6lR4/HHI\n6xm0caa8PIj0eXlQFLRxrtmXzTe+Ab//ffNtKyuD4F1bC1mE2fCjUgrCG9iUNZwRNxRzgGxyc2Hz\nZpUGJbOZ2avuPqqjdEkzNJ6ktoamcKtXZ5NXVhYs2LgRCguhuJiafdlMnQrr1x+8rR58EYktVcVI\nTDQ0hbvgAti7PzuI8jfcACUl7N2fzQUX0GZTOD34IhJbCuxysC60Q581K2gx0acP1NcHU23tZ+/7\n9AnWt9YUTg++iMSWArs0U7ktzNZ+k6k+ZwZ+441UnzODrf0mU7mt/eDe0BTukktorA8/99zgNTcX\nZs+mzaZwevBFJLYU2KVRNO3QmzWFuxZGjYJnnoHRo2HBAtptCtdQ2n/zhGLCo4rwvBBuhueFggdf\nTihus7QvIgdTYBcgNu3Qu9oUTg++iMSWmjsKAFu2wKBBBHXqM2YERe8GoRAsWwYlJWzdCgMHxv74\npaVBXfy6dbBoEbgHXwxXXw2nnQY9eqiNtEhnmzuqxC5Ak5YpxcVBu/NQCMyC16KioMliHFumRPvg\ni55aFfmMSuwCBIH09NODAntez3C77dCTrbPG0tKgGqmuLmhu+cgjQb39448HVTwrVqi0L+mhsyV2\nBXYBoHdv2LMneIJ0+XL43Oc+W7d3L5x/fvDEaO/esHt34vLZkp5alUyiJ0/lkDR0ydq0Hfr+/XDY\nYR23Q08kPbUqcjDVsQsQXTv0RNJTq12jexLpTYFdgOjaoSeSnlo9dOpJM/0psEujVOySVU+tHpqG\n5xVqayFcF+aaQUHXEdcMWk24LkxtLR0+ryDJT3Xs0syAAbBkSTClAg3Xdmh0TyIzqMQuKU1PrR4a\n3ZPIDCqxS0prOVzbokUluJeQNQ+u3hk8tZqM9wYSpeGeRNm4DRzW1j2Jn5W02m++pA6V2CXlpeK9\ngUTRPYnMoMAuaaHh3sDu3UH18e7dwbxK6s2pJ83MoMAuGS+T2nTrnkRmUJcCktEysZ8Z9aSZutS7\no0gHMrVNt+5JpD8F9jSUSVUL0Who0+31YcKTJjP0xzPgxhsp+MkMwpMm4/VhamqC0ny60T2J9KbA\nnmb0uHjnqU23pCsF9jSSqVULXaV+ZiRd6QGlNKLHxQ9NY5vu6uH8MDcvKKlHNLbprgj6oBdJJVGX\n2M3seDN73szeMLPXzez7sciYHDpVLRyaVG/TrXsp0iZ3j2oCjgVGRN73ArYBg9vbZuTIkS6xZ+Y+\nYYL7pzfcHMwELdmCycw//X8L/fTT3bOyEp3T5PDmm+65ue7PPede/2m9/8/iVf7glxb6/yxe5fWf\n1vuzzwbr33wz0Tk92FNPBXnLyXGfNStYNnNmMJ+bG6yX9ANUeCficszbsZvZ74Al7v6HttKoHXt8\nNAxvd9Oo1fxwy4zmVQt5IW4etIybKkqSbni7RErFNt0aDjBzJaQdu5nlA8OB8lbWXW5mFWZWUVVV\nFcvDSkSqVy0kQizadHd3lUgmN9OUTupMsb4zExACXgW+3lFaVcXERypXLaSqRFSJnHWWe3W1u69a\n5R4KNa9yC4XcV63y6uognaQXurMqxsxygNVAmbsv6ii9qmLiJxWrFlJVoqpEsrLg9NOhbNxCDvvR\njcEfuYEZ+2+4mTP/eAPr1wcNpCR9dLYqJurmjmZmwAPAls4EdYmv4uIg4JSVBVUL1dXNqxZU5xo7\niWpeqmaa0pFY1LGPBS4EzjCzjZHprBjsV7pIj4t3j1g0L+1K/bzupUiHOlNfE+tJdeySDqJtXtrV\n+nndS8lcJKq5Y2eojl3SQTTNS6Otn9e9lMykbntF4iyaKpFomyyq611pV2eK9bGeVBUj6SCaKhE1\nWZSuoJNVMeoETKSLBgwIRliqroZrr8tm0aIS3EvImgdX7wyqRFasaLsqpaQEysZt4LC2epb8WQnr\n13fPuUh6UVWMSBS6WiXS2GRxzXA8N6/ZusYmi+uCdCKHSoFdJEpdaV6qJosSTwrsIgkwb14Q2Gdf\nlo09XUblLct4uP/NVN6yDHu6jIsvzSYnB+bOTXROJRWpuaNIgqjJohwqNXcUSXJqsijxohK7iEiK\nUIldRCRDKbCLiKQZBXYRkTSjwJ6kNAK9iHSVAnsSKi0Nev67/36YOBGmTYMJE4L5goJgvYhIWxTY\nk0xlJUyfHnTnGq4Lc82g1bBwIdcMWk24LkxtbbBeJXcRaYs6AUsyiRpuTUTSh0rsSSYWw62JSGZT\nYE8yDd257i/fEInwTTR05zoV1qxJTP5EJPkpsCcZdecqItFSYE8y6s5VRKKlwJ5k1J2riERLnYAl\nIXXnKiKtUSdgKUzduYpINFRiFxFJESqxi4hkKAV2EZE0o8AuIpJmFNhFRNKMAruISJqJSWA3sylm\n9t9m9qaZLYjFPkVEpGuiDuxmlg3cAxQDg4EZZjY42v2KiEjXxKLEPgZ4093/1933A48B58ZgvyIi\n0gWxCOxfAN5pMr8jsqwZM7vczCrMrKKqqioGhxURkdZ0281Td/+lu49y91HHHHNMdx1WRCTjxCKw\n7wSObzLfN7JMREQSIBaB/c/ACWbW38wOA74JPBmD/YqISBdEPZi1u9eb2ZVAGZANPOjur0edMxER\n6ZKoAzuAuz8FPBWLfYmISHT05KmISJpRYBcRSTMK7CIiaUaBPY4qK2HOHOjbF1auDF7nzAmWi4jE\niwJ7nJSWQkEB3H8/TJwI06bBhAnBfEFBsF5EJB4U2OOgshKmT4faWqirg9mzg+WzZwfztbXBepXc\nRSQeFNjjYP9+qKkBd/D6MOM+Xg0LFzJ+92q8Pox7sL6uLtE5FZF0FJN27NLc/Pnw+OOQ1zMMkyeT\nU14ONTVNIePnAAAJkUlEQVTk5OVBURGUlVGzL5t58+D3v090bkUk3ajEHgelpVBSAvtWlkJ5OVRX\nB8X36mooL2ffylKmToU1axKdUxFJRwrscRAKwdq1sOL6DXhNTbN1XlPD8us3sm5dkE5EJNYU2ONg\n1izIyYH3jx0OuXnNV+bm8fdjC8nJgQsvTEz+RCS9KbDHwbx5QWAfeUMxFBUR/lyIAxjhz4WgqIgR\n1xeTkwNz5yY6pyKSjnTzNA4GDIAVK6B6bzY/GFbG1udKGcZGNu8rZGBhMeP3ZbNiRZBORCTWFNjj\npLg4aKdeVpbNH3uX8FR1CaEQfPFTGDxYQV1E4sfcvdsPOmrUKK+oqOj244qIpDIze9XdR3WUTnXs\nIiJpRoFdRCTNKLCLiKQZBXYRkTSjwC4ikmYU2EVE0owCu4hImlFgFxFJMwrsIiJpRoFdRCTNKLCL\niKQZBXYRkTSjwC4ikmYU2EVE0owCu4hImokqsJvZbWa21cw2m9lKM+sTq4wli8pKmDMH+vaFlSuD\n1zlzguUiIsko2hL7H4Ah7l4AbAP+LfosJY/SUigogPvvh4kTYdo0mDAhmC8oCNaLiCSbqAK7uz/t\n7vWR2ZeBvtFnKTlUVsL06VBbC3V1MHt2sHz27GC+tjZYr5K7iCSbWI55Ohv4rxjuL6H274eams/m\nP/00eB07FpqOJrh1a/fmS0SkIx2W2M3sGTN7rZXp3CZprgfqgaXt7OdyM6sws4qqqqrY5D6O5s9v\nHtgPP7z5KwTr583r3nyJiHQk6sGszewi4ApgkrvXdmabVBjMOisLTj8dVq+GvLyD19fUwNSpsH49\nhMPdnz8RyTzdMpi1mU0BfgCc09mgnipCIVi7Fi64APbubb5u795g+bp1QToRkWQSbauYJUAv4A9m\nttHM7o1BnpLCrFmQkwN9+kB9fTDV1n72vk+fYP2FFyY6pyIizUXbKuaf3f14dy+MTN+OVcYSbd68\nIHBfcgnk5sLmzXDuucFrbm7QOiYnB+bOTXRORUSai7qOvStSoY4dgnbq9fXwx+fDbF1cSiEb2GTD\nGTi3mPETsunRA4qLE51LEckUna1jj2Vzx7RTXAyV28Kc8J3J9KWcz1HDXs9jx38VkXNFGQNOzE50\nFkVEDqK+YjowYFspA3eXE6KabJwQ1QzcXc6AbXrsVESSkwJ7RzZsaN6gHYL5jRsTkx8RkQ4osHdk\n+PCDG7Ln5UFhYWLyIyLSAQX2jhQXQ1FR0GDdLHgtKtJdUxFJWrp52pHsbCgrC5rIbNwYlNSLi4Pl\nIiJJSIG9M7KzoaQkmEREkpyqYkRE0kzaB3aNgCQimSatA7tGQBKRTJS2gV0jIIlIpkrbm6caAUlE\nMlXaltg1ApKIZKq0DeylpUHrxJa9ATRoGAFpzZruzZeISLylbWDXCEgikqnSNrBrBCQRyVQpEdgb\n2qL37h0MMt27d8dt0ZuNgHR4mO1LVvP40IVsX7Ka3MPDGgFJRNJW0o+gVFoaNEusqwumBjk5wbRi\nRdv9cZWWQv2nYU76/mSO/Vs5udRQSx7vf7GIrXeW0ePwbPXlJSIpo7MjKCV1ib1lW/SmOtMWvbgY\nRlWV8sX3yukVGSijF9Uc/145o6pKFdRFJC0ldWC//faDA3pLdXWweHHb6499fwM965s3jelZX8Ox\nf9dAGSKSnpI6sP/6150L7I880k4CDZQhIhkmqQN7dXUM0mmgDBHJMEndpUAoBHv2dC5dmzRQhohk\nmKQO7LNmBT0xtlcd06m26BooQ0QySFJXxTS0RW+P2qKLiDSX1IF9wICgnXpu7sEBPicnWL5iRZBO\nREQCSR3YIagO37wZLr+8+ZOnl18eLNc9UBGR5pL+yVMREQmkxZOnIiJy6BTYRUTSjAK7iEiaSUgd\nu5lVAW93+4ET42jgg0RnIonp+nRM16h9mXR9+rn7MR0lSkhgzyRmVtGZmx2ZStenY7pG7dP1OZiq\nYkRE0owCu4hImlFgj79fJjoDSU7Xp2O6Ru3T9WlBdewiImlGJXYRkTSjwC4ikmYU2LvIzB40s/8z\ns9eaLLvNzLaa2WYzW2lmfdrYdruZ/dXMNppZWnaa08b1WRi5NhvN7GkzO66NbaeY2X+b2ZtmtqD7\nct29orxGGfkZarJunpm5mR3dxrYZ8Rlqk7tr6sIEnAaMAF5rsuxMoEfk/a3ArW1sux04OtHnkIDr\n07vJ+6uAe1vZLhuoBL4EHAZsAgYn+nyS6Rpl8mcosvx4oIzgIceDrkEmfYbamlRi7yJ3/yPwYYtl\nT7t7fWT2ZaBvt2csSbRxfT5pMpsHtHbnfgzwprv/r7vvBx4Dzo1bRhMoimuUEVq7PhGLgR/Q9rXJ\nmM9QWxTY42c2UNrGOgeeMbNXzezybsxTwpnZj8zsHWAm8MNWknwBeKfJ/I7IsozRiWsEGfoZMrNz\ngZ3uvqmdZBn/GVJgjwMzux6oB5a2kWScuxcCxcB3zey0bstcgrn79e5+PMG1uTLR+UlGnbxGGfcZ\nMrNc4Dra/rKTCAX2GDOzi4ASYKZHKvxacvedkdf/A1YS/HTMNEuB81pZvpOgDrVB38iyTNTWNcrU\nz9AAoD+wycy2E3w2/mJm/9QiXcZ/hhTYY8jMphDU/Z3j7rVtpMkzs14N7wluuB501z8dmdkJTWbP\nBba2kuzPwAlm1t/MDgO+CTzZHflLBp25Rpn6GXL3v7r75909393zCapYRrj7+y2SZvRnCBTYu8zM\nlgEvASeZ2Q4zuwRYAvQC/hBphnZvJO1xZvZUZNN/BF4ws03AK8Dv3X1NAk4hrtq4Pj81s9fMbDNB\nMPp+JG3j9YncfL6SoNXDFuBxd389IScRZ129RmT2Z6ittBn5GWqLuhQQEUkzKrGLiKQZBXYRkTSj\nwC4ikmYU2EVE0owCu4hImumR6AyIxJOZHQU8G5n9JyAMVEXma9391IRkTCSO1NxRMoaZ3QRUu/vP\nEp0XkXhSVYxkLDOrjrxOMLN1ZvY7M/tfM/upmc00s1cifZ4PiKQ7xsx+Y2Z/jkxjE3sGIq1TYBcJ\nDAO+DQwCLgROdPcxwP3A9yJp7gQWu/togj5c7k9ERkU6ojp2kcCf3f09ADOrBJ6OLP8rMDHy/ivA\nYDNr2Ka3mYXcvbpbcyrSAQV2kcCnTd4faDJ/gM/+T7KAU9x9X3dmTORQqSpGpPOe5rNqGcysMIF5\nEWmTArtI510FjIoMNv0GQZ28SNJRc0cRkTSjEruISJpRYBcRSTMK7CIiaUaBXUQkzSiwi4ikGQV2\nEZE0o8AuIpJm/j9JUAFO0b1t8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1d63e6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing\n",
    "\n",
    "with tf.Session() as sess:\n",
    "\n",
    "    saver.restore(sess, \"/tmp/my_model.ckpt\")\n",
    "        \n",
    "    X_new = time_series(\n",
    "        np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
    "        \n",
    "    y_pred = sess.run(\n",
    "        outputs, feed_dict={X: X_new})\n",
    "        \n",
    "    plt.title(\"Testing the model\", fontsize=14)\n",
    "    plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
    "    plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
    "    plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training across Many Time Steps\n",
    "* problem #1: RNNs susceptible to vanishing/exploding gradients issues. Previous tricks will work, but training time = prohibitively long for even modest sequences.\n",
    "* solution #1: *truncated backprop thru time* (unrolling RNN over limited number of timesteps during training). Works, but *model will not be able to learn long-term patterns*.\n",
    "* problem #2: memory of early inputs fades away - information lost during each transformation.\n",
    "* solution #2: using a *long-term memory cell*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Long Short-Term Memory (LSTM) Cell\n",
    "![lstm-cell](pics/lstm-cell.png)\n",
    "* implemented via *BasicLSTMCell()* instead of *BasicRNNCell()*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* key feature: net learns what to store (long-term), what to read from, what to throw away.\n",
    "* Four FC layers - each with unique purposes:\n",
    "    * main layer: outputs g(t)\n",
    "    * forget gate: controlled by f(t) - decides which parts of long-term memory to erase\n",
    "    * input gate: controlled by i(t) - decides which parts of g(t) to add to long-term memory\n",
    "    * output gate: controlled by o(t) - decides which parts of long-term state should be read & outputted at this time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "from tensorflow.contrib.layers import fully_connected\n",
    "\n",
    "n_steps = 28\n",
    "n_inputs = 28\n",
    "n_neurons = 150\n",
    "n_outputs = 10\n",
    "\n",
    "learning_rate = 0.001\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.int32, [None])\n",
    "\n",
    "lstm_cell = tf.contrib.rnn.BasicLSTMCell(\n",
    "    num_units=n_neurons)\n",
    "\n",
    "multi_cell = tf.contrib.rnn.MultiRNNCell(\n",
    "    [lstm_cell]*3)\n",
    "\n",
    "outputs, states = tf.nn.dynamic_rnn(\n",
    "    multi_cell, X, dtype=tf.float32)\n",
    "\n",
    "top_layer_h_state = states[-1][1]\n",
    "\n",
    "logits = fully_connected(\n",
    "    top_layer_h_state, \n",
    "    n_outputs, \n",
    "    activation_fn=None, scope=\"softmax\")\n",
    "\n",
    "xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "    labels=y, logits=logits)\n",
    "\n",
    "loss = tf.reduce_mean(\n",
    "    xentropy, name=\"loss\")\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(\n",
    "    learning_rate=learning_rate)\n",
    "\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "correct = tf.nn.in_top_k(\n",
    "    logits, y, 1)\n",
    "\n",
    "accuracy = tf.reduce_mean(\n",
    "    tf.cast(correct, tf.float32))\n",
    "    \n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_2:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_3:0' shape=(?, 150) dtype=float32>),\n",
       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_4:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_5:0' shape=(?, 150) dtype=float32>),\n",
       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_6:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_layer_h_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 Train accuracy = 0.966667 Test accuracy = 0.9403\n",
      "Epoch 1 Train accuracy = 0.98 Test accuracy = 0.9742\n",
      "Epoch 2 Train accuracy = 0.993333 Test accuracy = 0.979\n",
      "Epoch 3 Train accuracy = 0.993333 Test accuracy = 0.9805\n",
      "Epoch 4 Train accuracy = 1.0 Test accuracy = 0.9854\n",
      "Epoch 5 Train accuracy = 0.98 Test accuracy = 0.9827\n",
      "Epoch 6 Train accuracy = 0.993333 Test accuracy = 0.9851\n",
      "Epoch 7 Train accuracy = 1.0 Test accuracy = 0.9865\n",
      "Epoch 8 Train accuracy = 1.0 Test accuracy = 0.9887\n",
      "Epoch 9 Train accuracy = 0.993333 Test accuracy = 0.9871\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 10\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        for iteration in range(mnist.train.num_examples // batch_size):\n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            X_batch = X_batch.reshape((batch_size, n_steps, n_inputs))\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
    "        print(\"Epoch\", epoch, \"Train accuracy =\", acc_train, \"Test accuracy =\", acc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peephole Connections\n",
    "* Basic LSTM cell: gate controllers only see input x(t) & prev short-term state h(t-1).\n",
    "* Improvement: let gate peek at long-term state too. Provided with previous long-term state c(t-1) as inputs to forget gate & input gate; current long-term state c(t) added as input to output gate controller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Peepholes in TF\n",
    "lstm_cell = tf.contrib.rnn.LSTMCell(\n",
    "    num_units=n_neurons, \n",
    "    use_peepholes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gated Recurrent Unit (GRU) Cell\n",
    "* Simplified version of LSTM cell\n",
    "* State vectors merged into single h(t).\n",
    "* Single gate controller manages forget gate & input gate. (if a memory is to be stored, its location is erased first.)\n",
    "* No output gate - full state vector output on \n",
    "![gru-cell](pics/gru-cell.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# in TF\n",
    "gru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Natural Language Processing (NLP)\n",
    "* Mostly based on RNNs\n",
    "* See [Word2Vec](https://goo.gl/edArdi) and [Seq2Seq](https://goo.gl/L82gvS) tutorials!\n",
    "* More: [Chris Olah](https://goo.gl/5rLNTj), [Sebastian Ruder](https://goo.gl/ojJjiE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Embeddings\n",
    "* First: need a word representation. Similar words should have similar representations.\n",
    "* Common sol'n: each word in vocab = small, dense vector of embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create embeddings variable. init with random[-1,+1]\n",
    "\n",
    "vocabulary_size = 50000\n",
    "embedding_size = 150\n",
    "embeddings = tf.Variable(\n",
    "    tf.random_uniform(\n",
    "        [vocabulary_size, embedding_size], \n",
    "        -1.0, 1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Feeding new sentences to net: replace unknown words, numbers, URLs, etc with predefined tokens. Once a word is known, you can look it up in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_inputs = tf.placeholder(\n",
    "    tf.int32, shape=[None]) # from ids...\n",
    "\n",
    "embed = tf.nn.embedding_lookup(\n",
    "    embeddings, train_inputs) # ...to embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### English => French Encoder-Decoder Network ([link](https://goo.gl/0g9zWP))\n",
    "* English inputs, French outputs\n",
    "* French translations also fed, pushed back one step\n",
    "* English sentences **reversed** before entry (ensures beginning of sentence is fed last = best for decoder translation)\n",
    "* Decoder returns score for each word in output vocabulary - softmax turns them into probabilities. Highest probability word is returned.\n",
    "![encoder-decoder](pics/encoder-decoder.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from six.moves import urllib\n",
    "\n",
    "import errno\n",
    "import os\n",
    "import zipfile\n",
    "\n",
    "WORDS_PATH = \"datasets/words\"\n",
    "WORDS_URL = 'http://mattmahoney.net/dc/text8.zip'\n",
    "\n",
    "def mkdir_p(path):\n",
    "    \"\"\"Create directories, ok if they already exist.\n",
    "    \n",
    "    This is for python 2 support. In python >=3.2, simply use:\n",
    "    >>> os.makedirs(path, exist_ok=True)\n",
    "    \"\"\"\n",
    "    try:\n",
    "        os.makedirs(path)\n",
    "    except OSError as exc:\n",
    "        if exc.errno == errno.EEXIST and os.path.isdir(path):\n",
    "            pass\n",
    "        else:\n",
    "            raise\n",
    "\n",
    "def fetch_words_data(words_url=WORDS_URL, words_path=WORDS_PATH):\n",
    "    os.makedirs(words_path, exist_ok=True)\n",
    "    zip_path = os.path.join(words_path, \"words.zip\")\n",
    "    if not os.path.exists(zip_path):\n",
    "        urllib.request.urlretrieve(words_url, zip_path)\n",
    "    with zipfile.ZipFile(zip_path) as f:\n",
    "        data = f.read(f.namelist()[0])\n",
    "    return data.decode(\"ascii\").split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anarchism', 'originated', 'as', 'a', 'term']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = fetch_words_data()\n",
    "words[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "vocabulary_size = 50000\n",
    "\n",
    "vocabulary = [(\"UNK\", None)] + Counter(words).most_common(vocabulary_size - 1)\n",
    "vocabulary = np.array([word for word, _ in vocabulary])\n",
    "\n",
    "dictionary = {word: code for code, word in enumerate(vocabulary)}\n",
    "\n",
    "data = np.array([dictionary.get(word, 0) for word in words])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('anarchism originated as a term of abuse first used',\n",
       " array([5244, 3081,   12,    6,  195,    2, 3135,   46,   59]))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join(words[:9]), data[:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'anywhere originated as a term of presidency first used'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join([vocabulary[word_index] for word_index in [5241, 3081, 12, 6, 195, 2, 3134, 46, 59]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('culottes', 0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words[24], data[24]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "from collections import deque\n",
    "\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "    global data_index\n",
    "    assert batch_size % num_skips == 0\n",
    "    assert num_skips <= 2 * skip_window\n",
    "    batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "    span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
    "    buffer = deque(maxlen=span)\n",
    "    for _ in range(span):\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "    for i in range(batch_size // num_skips):\n",
    "        target = skip_window  # target label at the center of the buffer\n",
    "        targets_to_avoid = [ skip_window ]\n",
    "        for j in range(num_skips):\n",
    "            while target in targets_to_avoid:\n",
    "                target = random.randint(0, span - 1)\n",
    "            targets_to_avoid.append(target)\n",
    "            batch[i * num_skips + j] = buffer[skip_window]\n",
    "            labels[i * num_skips + j, 0] = buffer[target]\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "    return batch, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_index=0\n",
    "batch, labels = generate_batch(8, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3081, 3081,   12,   12,    6,    6,  195,  195], dtype=int32),\n",
       " ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term'])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch, [vocabulary[word] for word in batch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[5244],\n",
       "        [  12],\n",
       "        [   6],\n",
       "        [3081],\n",
       "        [ 195],\n",
       "        [  12],\n",
       "        [   6],\n",
       "        [   2]], dtype=int32),\n",
       " ['anarchism', 'as', 'a', 'originated', 'term', 'as', 'a', 'of'])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels, [vocabulary[word] for word in labels[:, 0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 128  # Dimension of the embedding vector.\n",
    "skip_window = 1       # How many words to consider left and right.\n",
    "num_skips = 2         # How many times to reuse an input to generate a label.\n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors. Here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent.\n",
    "\n",
    "valid_size = 16     # Random set of words to evaluate similarity on.\n",
    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = rnd.choice(valid_window, valid_size, replace=False)\n",
    "num_sampled = 64    # Number of negative examples to sample.\n",
    "\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "# Input data.\n",
    "train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "\n",
    "# Look up embeddings for inputs.\n",
    "init_embeddings = tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)\n",
    "embeddings = tf.Variable(init_embeddings)\n",
    "embed = tf.nn.embedding_lookup(embeddings, train_inputs)\n",
    "\n",
    "# Construct the variables for the NCE loss\n",
    "nce_weights = tf.Variable(\n",
    "    tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                        stddev=1.0 / np.sqrt(embedding_size)))\n",
    "nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "\n",
    "# Compute the average NCE loss for the batch.\n",
    "# tf.nce_loss automatically draws a new sample of the negative labels each\n",
    "# time we evaluate the loss.\n",
    "loss = tf.reduce_mean(\n",
    "    tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed,\n",
    "                   num_sampled, vocabulary_size))\n",
    "\n",
    "# Construct the Adam optimizer\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "# Compute the cosine similarity between minibatch examples and all embeddings.\n",
    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=1, keep_dims=True))\n",
    "normalized_embeddings = embeddings / norm\n",
    "valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n",
    "similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "\n",
    "# Add variable initializer.\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0\tAverage loss at step  0 :  260.603485107\n",
      "Nearest to and: marsh, sipe, vehement, exercises, einer, mrnas, dancer, grendel,\n",
      "Nearest to called: innuendo, algerian, synthesizing, montgomery, unspoken, elevating, plankton, monochromatic,\n",
      "Nearest to many: salinas, fuji, trochaic, rubinstein, eln, tintin, lloyd, carbides,\n",
      "Nearest to about: moreover, congo, choctaws, accomplished, unwieldy, ks, halifax, pac,\n",
      "Nearest to than: awake, exact, offutt, gloster, pronunciations, delight, tsarina, hopped,\n",
      "Nearest to or: long, mage, warriors, adhering, sk, clitoridectomy, parenting, vanguard,\n",
      "Nearest to of: shakespeare, kemp, relax, cul, breakaway, solemnly, mason, mng,\n",
      "Nearest to when: tolstoy, courtesan, hashes, coursing, evi, ren, diurnal, stimson,\n",
      "Nearest to four: supermassive, soviet, palatalization, acclaimed, aided, whitney, filtration, lesbians,\n",
      "Nearest to most: din, hawaii, loch, necronomicon, sunnah, sh, onager, miracles,\n",
      "Nearest to on: helpers, tangle, heretical, compulsion, unorganized, rump, intimidating, israeli,\n",
      "Nearest to but: ohio, rican, politeness, watkins, ingesting, street, hatred, novices,\n",
      "Nearest to that: xhosa, distressed, continually, fausto, iole, admitted, etsi, gross,\n",
      "Nearest to all: orissa, persistent, moro, informative, reservation, ren, browne, frobenius,\n",
      "Nearest to in: chanced, accelerator, sergio, demonstrating, inertia, jarrett, intricate, orange,\n",
      "Nearest to had: irredentist, kbit, sarris, lactate, bettor, narratives, hui, transpired,\n",
      "Iteration: 999\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"
     ]
    }
   ],
   "source": [
    "num_steps = 1000 # was 100000?\n",
    "\n",
    "with tf.Session() as session:\n",
    "    init.run()\n",
    "\n",
    "    average_loss = 0\n",
    "    for step in range(num_steps):\n",
    "        print(\"\\rIteration: {}\".format(step), end=\"\\t\")\n",
    "        batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)\n",
    "        feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}\n",
    "\n",
    "        # We perform one update step by evaluating the training op (including it\n",
    "        # in the list of returned values for session.run()\n",
    "        _, loss_val = session.run([training_op, loss], feed_dict=feed_dict)\n",
    "        average_loss += loss_val\n",
    "\n",
    "        if step % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss /= 2000\n",
    "            # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "            print(\"Average loss at step \", step, \": \", average_loss)\n",
    "            average_loss = 0\n",
    "\n",
    "        # Note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "        if step % 10000 == 0:\n",
    "            sim = similarity.eval()\n",
    "            for i in range(valid_size):\n",
    "                valid_word = vocabulary[valid_examples[i]]\n",
    "                top_k = 8 # number of nearest neighbors\n",
    "                nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "                log_str = \"Nearest to %s:\" % valid_word\n",
    "                for k in range(top_k):\n",
    "                    close_word = vocabulary[nearest[k]]\n",
    "                    log_str = \"%s %s,\" % (log_str, close_word)\n",
    "                print(log_str)\n",
    "\n",
    "    final_embeddings = normalized_embeddings.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save final embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.save(\"my_final_embeddings.npy\", final_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_with_labels(low_dim_embs, labels):\n",
    "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
    "    plt.figure(figsize=(18, 18))  #in inches\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i,:]\n",
    "        plt.scatter(x, y)\n",
    "        plt.annotate(label,\n",
    "                     xy=(x, y),\n",
    "                     xytext=(5, 2),\n",
    "                     textcoords='offset points',\n",
    "                     ha='right',\n",
    "                     va='bottom')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Et7c3y5cv5/vvvze9/+CDD1Y5fsiQIUDF0prK65WUlDBu3Dg8PDwIDw8nMzPzmrXt2rWL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nu3uF7tjUX5XdWlMTW1i4iIiLQkxECDyFNn1KhRjBo1SqnNy8tL5bEeHh78XMliKu/mbl5+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\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IZDJyc3N57733ePDgAc7OzvTr10/oe+3aNT799FO2bNmCjY0N69evR1dXl+joaAoKCnB1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4UOnxbt260a1btzLPN336dOlLu8KECROYMGECUJQZcPr0aVatWkV0dDSWlpZMmTIFFxcXPv/8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd092a58160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "plot_only = 500\n",
    "low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:])\n",
    "labels = [vocabulary[i] for i in range(plot_only)]\n",
    "plot_with_labels(low_dim_embs, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
